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  • 1. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME TECHNOLOGY (IJCET) ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 4, Issue 6, November - December (2013), pp. 01-08 © IAEME: www.iaeme.com/ijcet.asp Journal Impact Factor (2013): 6.1302 (Calculated by GISI) www.jifactor.com IJCET ©IAEME DETECTION AND CLASSIFICATION OF NON PROLIFERATIVE DIABETIC RETINOPATHY STAGES USING MORPHOLOGICAL OPERATIONS AND SVM CLASSIFIER Preethi N Patil1, G. G. Rajput1 1 Dept. of Computer Science, Gulbarga University, Gulbarga, Karnataka, India ABSTRACT Diabetic retinopathy (DR) is one of the complications of diabetes mellitus that is considered as the major cause of vision loss among people around the world. The most important signs of diabetic retinopathy are dark lesions (i.e. microaneurysms and hemorrhages) and bright lesions (i.e. hard exudates and cotton wool spots). In this paper, we present an efficient method to grade the severity of DR in retinal images. Morphological operations are used to detect the pathologies associated with DR, namely, blood vessels, microaneurysms and hard exudates. SVM classifier is used to grade the retinal image under the categories of Non Proliferative DR (NPDR) namely, normal (no DR), mild NPDR, moderate NPDR and severe NPDR. The proposed method successfully classified the subjects into normal, mild NPDR, moderate NPDR and severe NPDR with an accuracy of 100%, 93.33%, 100% and 86.67% respectively. An average sensitivity of 96.08% and an average specificity of 97.92% is reported. Keywords: Diabetic Retinopathy, Hard Exudates, Microaneurysms, Optic Disc, Morphological Operations, Canny Edge Detector. 1. INTRODUCTION Diabetes mellitus is a growing health problem in developing countries. According to the Diabetes Atlas, India with 40.9 million people with diabetes has already become the 'Diabetes Capital of the World' and this number is set to increase to 69.9 million by 2025 [1]. The prevalence of diabetes is growing rapidly in both urban and rural areas in India. In 1972, the prevalence of diabetes in urban areas was 2.1% [2] and this has rapidly climbed to 12-16% representing a 600-800% increase in prevalence rates over a 30 year period [3, 4]. Till recently, the prevalence of diabetes was reported 1
  • 2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME to be low in rural areas, but recent studies suggest that the prevalence rate is rapidly increasing even in rural areas, [5] similar to the situation seen in developed countries of the world. Diabetic retinopathy is one of the complications of diabetes mellitus that occurs when blood vessels in the retina change. Sometimes these vessels swell and leak fluid or even close off completely. In other cases, abnormal new blood vessels grow on the surface of the retina. Diabetic retinopathy usually affects both eyes. People who have diabetic retinopathy often don't notice changes in their vision in the disease's early stages. But as it progresses, acute vision loss occurs, making it the primary cause of blindness. There are two types of diabetic retinopathy, namely, Non-proliferative diabetic retinopathy (NPDR) and Proliferative diabetic retinopathy (PDR). NPDR is the earliest stage of diabetic retinopathy. With this condition, damaged blood vessels in the retina begin to leak extra fluid and small amounts of blood into the eye. Sometimes, deposits of cholesterol or other fats from the blood may leak into the retina. NPDR can cause changes in the eye, including: • • • Microaneurysms-small bulges in blood vessels of the retina that often leak fluid. Retinal hemorrhages - tiny spots of blood that leak into the retina. Hard exudates - deposits of cholesterol or other fats from the blood that have leaked into the retina. PDR mainly occurs when many of the blood vessels in the retina close, preventing enough blood flow. In an attempt to supply blood to the area where the original vessels closed, the retina responds by growing new blood vessels. There are many algorithms and techniques available in the literature for the detection of pathologies causing Diabetic retinopathy. The state of art methods have been described below. U R Acharya et. al. [7] have presented a method of classifying the DR stages by computing the area of four features, namely, blood vessels, hemorrhages, microaneurysms and exudates. Gardner, G Keatting, willamson, T and Elliott, A [8] used neural network to detect diabetic features, namely, vessels, exudates and hemorrhages in fundus images and compared the performance with an ophthalmologist screening method. Blood vessels, exudates, and hemorrhages were detected with accuracy rates of 91.7%, 93.1% and 73.8%, respectively. Niemeijer et al. [9] presented a method to detect red lesions based on hybrid approach. They were able to detect the red lesions with a sensitivity of 100% and a specificity of 87%. Nayak et al[10] have classified the fundus images into normal, NPDR and PDR classes with an accuracy of 93%. Acharya U. R [11] used SVM classifier to categories the fundus images into normal, mild, moderate, severe DDR and PDR. Akara Sopharak[12] presented an automatic microaneurysm detection from non-dilated Diabetic Retinopathy retinal images using mathematical morphology. They were able to detect the microaneurysms with 81.61% sensitivity and 99.99% specificity. Li et al. and O. Chutatape,[13] proposed a method that divides the image into 64 sub-images followed by application of region growing and edge detection to detect exudates. C. I. Sanchez, M. Garcia, A. Mayo, M. Lopez and R. Hornero,[14] proposed a method based on mixture models to separate exudates from background followed by edge detection technique to distinguish hard exudates from soft exudates. Garcia et al., [15] proposed a combination of local and global thresholding to segment exudates followed by investigating three neural network classifiers to classify exudates. Akara Sopharaket. al.[16] have tuned the morphological operations to detect the exudates with a sensitivity and specificity of 80% and 99.4% respectively. Using back propagation neural network, exudates detection is presented in [17]. A comparative study on machine learning and traditional approaches for exudates detection is 2
  • 3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME presented in [18]. Applying fuzzy C-means clustering techniques to low contrast fundus images with non-dilated pupils, Akara Sopharak et. al. [19] have reported exudates detection with 92.18% sensitivity and 91.52% specificity. To detect the DR from fundus images, it is essential to detect and eliminate optic disc. Several methods have been reported in the literature for optic disc detection [20. 21, 22]. From the literature survey, it is observed that the fundus images used for performing the experiments are either from the standard dataset [23] or have been collected locally and that the number of images used is limited. In this paper, we present an efficient method for automatic classification of DR using morphological operations. The work presented is modification to the method proposed in [7] in consultation with senior ophthalmologist. Further, to validate the proposed method we perform experiments on a large dataset collected from a research center [27]. In the proposed method, we consider three features namely, blood vessels, microaneurysms and exudates to detect diabetic retinopathy stages. The fundus image is divided into four quadrants and the area of microaneurysms and exudates is computed in each of the four quadrants. SVM classifier is adopted to classify the fundus image into different stages of NPDR. 2. MATERIALS The fundus images required for performing experiment are collected from Karnataka Institute of Diabetology, Bangalore. The experiments are performed on 337 digital color fundus photographs, of which 321 fundus images of dimension 4288X2848, captured by NIKON D300 camera. And 16 fundus images of dimension 786X584, captures by a Canon CR5 non-mydriatic 3CCD camera are selected from DRIVE dataset [23]. 3. PROPOSED METHOD There are three contributions in the proposed work first, pre processing the local database images [24], second, implementation of 4-2-1 rule for classification of NPDR into different stages. Third, deciding the structuring element for detecting blood vessels and microaneurysms that works well for the local database and the standard DRIVE dataset [23]. Features are computed from the pre processed fundus image in reference with ophthalmologist. Distribution of microaneurysms and exudates on retinal surface are computed by dividing the fundus image into four quadrants to facilitate implementation of 4-2-1 rule of NPDR classification. The following section describes feature extraction methods. 3.1 Blood Vessel Detection The block diagram for extracting the blood vessels is shown in figure 1. Morphological operations are applied to the pre processed input image to extract blood vessels followed by optic disk detection and elimination [25]. Empirically, ball shaped structuring element of size 8 is used to detect the blood vessels. Due to elimination of optic disk, the blood vessels in it are also lost; hence morphological reconstruction is applied to retrieve the lost blood vessels. At this stage, segmentation is performed to eliminate other features like microaneurysms and exudates. Finally, the area of the blood vessels is calculated. Figure 2 presents few examples of blood vessels detected. In normal case, retina contains healthy blood vessel network (fig.2.a), hence the area occupied by the blood vessels is more. The area of blood vessel in retina affected by mild or moderate NPDR is less as compare to normal retinal (fig.2.b, 2.c). And it is very less in retina affected by severe NPDR (fig.2.d). 3
  • 4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME Fig. 1. Block Diagram for Blood Vessel Extraction Fig: 2. (a) Normal, (b)Mild NPDR, (c) Moderate NPDR, (d) Severe NPDR 3.2 Microaneurysms Detection The block diagram for extracting microaneurysms is shown in figure 3. The pre-processed image is segmented in two stages. Firstly, edge detection is achieved by using canny edge detector as it is used to detect edges in a very robust manner [26]. Secondly, noise and other non-microaneurysm features like exudates are eliminated by applying thresholding technique. Next, morphological operations with disk shaped structuring element of size 6 are used to eliminate blood vessel network. The resulting image with only microaneurysms is then divided into four quadrants and area occupied by microaneurysms in each of the quadrants is computed. Few example images of microaneurysms detection is shown in figure 4. Obviously, normal retinal do not contain microaneurysms (fig.4.a), retinal affected with mild NPDR contains at least one microaneurysm but limited to few in one quadrant (fig.4.b). Moderate NPDR retina contains few microaneurysms distributed in at least two quadrants (fig.4.c) and in case with severe NPDR numerous microaneurysms are present in all the four quadrants (fig.4.d). Fig. 2. Block Diagram for Microaneurysms Extraction 4
  • 5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME Fig. 3. (a) Normal, (b)Mild NPDR, (c) Moderate NPDR, (d) Severe NPDR 3.3 Exudates Detection The block diagram for exudates detection is shown in figure 5. First, to facilitate exudates detection, the optic disc is located and eliminated [23]. Thresholding and morphological operations are then applied to detect the exudates. Few example images of exudates detection is shown in figure 6. Normal retinal image do not contain exudates (fig.6.a), retina affected with mild NPDR may or may not contain exudates, but if present they are limited to few in only one quadrant (fig.6.b). Retina with moderate NPDR contains few exudates distributed in at least two quadrants (fig.6.c) and in retinal with severe NPDR numerous exudates are present in more than three quadrants (fig.6.d). Fig. 4. Block Diagram for Exudates Extraction Fig: 6.(a) Normal, (b)Mild NPDR, (c) Moderate NPDR, (d) Severe NPDR 5
  • 6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME 4. RESULTS The proposed method has been evaluated using 337 fundus images collected from the Karnataka Institute of Diabetology, Bangalore. All the three features of Diabetic retinopathy have been detected successfully. In the normal images the blood vessels occupy the larger area and microaneurysms and exudates are absent. In case of mild NPDR and moderate NPDR, the microaneurysms and exudates showed their presence and in severe NPDR their prominence is more. The SVM classifier has been used for classification. The features extracted were fed to SVM classifier for classified the fundus images as normal, mild NPDR, moderate NPDR, and severe NPDR. An average accuracy of 100%, 93.33%, 100% and 86.67% is obtained for normal, mild NPDR, moderate NPDR, and severe NPDR, respectively. Sensitivity of 96.08% and specificity of 97.92% is observed. The details of the classification obtained are presented in Table1. The recognition results in case of sever NPDR is low compared to other stages, since many of the fundus images with severe NPDR were misclassified as moderate NPDR. Table 1. DR recognition result using SVM classifier Stages No. of data sets used for training No. of data sets used for testing % of correct classification Normal 22 15 100 Mild NPDR 60 40 93.33 Moderate NPDR 60 40 100 Sever NPDR 60 40 86.67 5. CONCLUSION In this paper, an automated method for detecting different stages of Non Proliferative Diabetic Retinopathy stages is implemented using pathologies associated with DR namely, blood vessels, exudates and microaneurysms. The proposed method is efficient in terms of number of features used and recognition accuracy as against [7] in the literature. The classification of stages of NPDR is based upon the presence of exudates and microaneurysms and their distribution in four quadrants of the retinal images. The results are demonstrated for a large dataset of fundus images that includes local dataset and DRIVE dataset. The system is able to classify the NPDR stages into normal, mild NPDR, moderate NPDR and severe NPDR with an average accuracy of 95%, an average sensitivity of 96.08% and an average specificity of 97.92%. As observed from the results, we are working towards improving the feature set to increase the recognition accuracy for severe NPDR cases. 6. ACKNOWLEDGMENT This research is funded by UGC under UGC MRP F.40-257/2011(SR) and DST under INSPIRE fellowship. We would like to thank Karnataka Institute of Diabetology, Bangalore, Karnataka, for extending support by providing the fundus images to carry out the research work. 6
  • 7. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME 7. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] Sicree R, Shaw J, Zimmet P. Diabetes and impaired glucose tolerance. In: Gan D, editor. Diabetes Atlas. International Diabetes Federation. 3rd ed. Belgium: International Diabetes Federation; 2006. p. 15-103. Ahuja MM. Epidemiology studies on diabetes mellitus in India. In. Ahuja MM, editor. Epidimiology of diabetes in developing countries, Interprint. New Delhi: 1979. p. 29-38. Ramachandran A, Snehalatha C, Kapur A, Vijay V, Mohan V, Das AK, et al. Diabetes Epidemiology Study Group In India (DESI): High prevalence of diabetes and impaired glucose tolerance in India:National Urban Diabetes Survey. Diabetologia 2001;44:1094101Mohan V, Deepa M, Deepa R, Shanthirani CS, Farooq S, Ganesan A, et al . Secular trends in the prevalence of diabetes and impaired glucose tolerance in urban South India-the Chennai Urban Rural Epidemiology Study (CURES-17). Diabetologia 2006;49:1175-8 Mohan V, Mathur P, Deepa R, Deepa M, Shukla DK, Menon GR, et al. Urban rural differences in prevalence of self-reported diabetes in India: The WHO-ICMR Indian NCD risk factor surveillance. Diab Res Clin Pract 2008; 80:15 9-68. Chow CK, Raju PK, Raju R, Reddy KS, Cardona M, Celermajer DS, et al. The prevalence and management of diabetes in rural India. Diabetes Care 2006; 29:17 17-8. http://www.icoph.org/downloads/Diabetic-Retinopathy-Detail.pdf. U R Acharya, C M Lim, E Y K Ng, C Chee and T Tamura, Computer-based detection of diabetic retinopathy stages using digital fundus images: Part h: journal of engineering in Medicine 2009 223:545. Gardner, G., keating, D., Williamson, T., and Elliott, A. Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. Br J Ophthalmol 80:940-944, 1996. Niemeijer, M, van Ginneken, B., Staal, J., Suttorp-Schulten, M., and Abramoff, M. Automatic detection of red lesions in digital color fundus photographs. IEEE transmed imaging 24:584592, 2005. Nayak,J., Bhat,P.S., Acharya,U.R., Lim,C.M., and Kagathi,M. Automated identification of different stages of diabetic retinopathy using digital fundus images. J.Med. Systems, 2008, 32(2), 107-115. Kandiraju,N., Dua,S., and Thompson, H.W. Design and implementation of a unique blood vessel detection algorithm towards early diagnosis of diabetic retinopathy. In proceedings of the International Conference on Information technology: Coding and computing (ITCC’05), 2005, pp 26-31(IEEE Computer society, New York). Akara Sopharak, Bunyarit Uyyanonvara and Sarah Barman, Automatic Microaneurysm Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Mathematical Morphology Methods. IAENG International Journal of Computer Science, 38:3, IJCS_38_3_15. H. Li and O. Chutatape, “Automated feature extraction in color retinal images by a model based approach,” IEEE Trans. on Medical Engineering, vol. 51, pp. 246-254, 2004 C. I. Sanchez, M. Garcia, A. Mayo, M. Lopez and R. Hornero, “Retinal image analysis based on mixture models to detect hard exudates,” Medical Image Analysis, vol. 13, pp. 650-658, 2009. M. Garcia, C. I. Sanchez, M. I. Lopez, D. Abasolo and R. Hornero, “Neural network based detection of hard exudates in retinal images,” Computer Methods and Programs in Biomedicine, vol. 93, pp. 9-19, 2009. 7
  • 8. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME [16] AkaraSopharak and BunyaritUyyanonvara proposed an “Automatic exudates detection on thai diabetic retinopathy patients retinal images” Proceedings of the 2006 ECTI International Conference, pp.709-712; May (2006). [17] AshagowdaKaregowda, AsfiyaNasiha, M.A. Jayaram and A.S. Manjunath proposed an “Exudates detection in retinal image using back propagation neural network” International Journal of Computer Applications (0975 – 8887) Volume 25– No.3, July 2011. [18] AkaraSopharak, BunyaritUyyanonvara, Sarah Barman and Thomas Williamson “Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods” Computerized Medical Imaging and Graphics 32 (2008) 720–727. [19] AkaraSopharak, BunyaritUyyanonvara, Sarah Barman proposed an “Automatic exudates detection fordiabetic retinopathy screening” doi:10.2306/scienceasia1513-1874.2009.35.080. [20] Kittipolwisaing, NualsawatHiranskolwong and EkkaratPothiruk proposed an “Automatic optic disc detection form low contrast retinal images” Applied Mathematical Sciences, Vol. 6, 2012, no. 103, 5127 – 5136. [21] Siddalingaswamy P. C. and GopalakrishnaPrabhu .K proposed”, Automatic Localization and Boundary Detection of Optic Disc Using Implicit Active Contours”. 2010 International Journal of Computer Applications (0975 – 8887)Volume 1 – No. 7. [22] ViraneeThongnuch and BunyaritUyyanonvara proposed “Automatic optic disc detection from low contrast retinal image of ROP infant usinf GVF snake” Suranaree J. Sci. Technol. Vol. 14 No. 3; July-September 2007. [23] http://www.isi.uu.nl/Reseaech/Databases/Drive/. [24] Dr. G. G. Rajput, Preethi N Patil, Ramesh Chavan, “Automatic Detection of Microaneurysms from Fundus Image Using Morphological Operations”, Lecture Notes in Electrical Engineering 258, DOI: 10.1007/978-81-322-1524-0_37. [25] Dr. G. G. Rajput, Preethi N Patil, Ramesh Chavan, “Automatic Detection of Hard and Soft Exudates from Fundus Images Using Morphological Operations”, 2nd National Conference on Innovative Paradigms in Engineering & Technology(NCIPET), 2013, pp.1-4. [26] Rafael C. Gonzalez, Richard Eugene Woods, Steven L. Eddins “Digital image processing using matlab” Pearson Education India. [27] http://kidbangalore.in. [28] Y. Angeline Christobel and P. Sivaprakasam, “Improving the Performance of K-Nearest Neighbor Algorithm for the Classification of Diabetes Dataset with Missing Values”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 3, 2012, pp. 155 - 167, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [29] R.Pushpavalli and G.Sivaradje, “Nonlinear Filtering Technique for Preserving Edges and Fine Details of Digital Images”, International Journal of Electronics and Communication Engineering & Technology (IJECET), Volume 3, Issue 1, 2012, pp. 29 - 40, ISSN Print: 0976- 6464, ISSN Online: 0976 –6472. [30] Ankit Vidyarthi and Ankita Kansal, “A Survey Report on Digital Images Segmentation Algorithms”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 2, 2012, pp. 85 - 91, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. 8